Holographic Reduced Representations for Oscillator Recall: A Model of Phonological Production

This paper describes a new computational model of phonological production, Holographic Reduced Representations for Oscillator Recall, or HORROR. HORROR's architecture accounts for phonological speech error patterns by combining the hierarchical oscillating context signal of the OSCAR serial-order model~\cite{VousdenEtAl:2000,BrownEtAl:2000} with a holographic associative memory~\cite{Plate:1995}. The resulting model is novel in a number of ways. Most importantly, all of the noise needed to generate errors is intrinsic to the system, instead of being generated by an external process. The model features fully-distributed hierarchical phoneme representations and a single distributed associative memory. Using fewer parameters and a more parsimonious design than OSCAR, HORROR accounts for error type proportions, the syllable-position constraint, and other constraints seen in the human speech error data

Fractional Quantum Hall Effect via Holography: Chern-Simons, Edge States, and Hierarchy

We present three holographic constructions of fractional quantum Hall effect (FQHE) via string theory. The first model studies edge states in FQHE using supersymmetric domain walls in N=6 Chern-Simons theory. We show that D4-branes wrapped on CP^1 or D8-branes wrapped on CP^3 create edge states that shift the rank or the level of the gauge group, respectively. These holographic edge states correctly reproduce the Hall conductivity. The second model presents a holographic dual to the pure U(N)_k (Yang-Mills-)Chern-Simons theory based on a D3-D7 system. Its holography is equivalent to the level-rank duality, which enables us to compute the Hall conductivity and the topological entanglement entropy. The third model introduces the first string theory embedding of hierarchical FQHEs, using IIA string on C^2/Z_n.Comment: 36 pages, 6 figures; v2: with an improved derivation of Hall conductivity in section 3.2, typo corrections, and additional references; v3: explanations and comments adde

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin

Holographic principle and large scale structure in the universe

A reasonable representation of large scale structure, in a closed universe so large it's nearly flat, can be developed by extending the holographic principle and assuming the bits of information describing the distribution of matter density in the universe remain in thermal equilibrium with the cosmic microwave background radiation. The analysis identifies three levels of self-similar large scale structure, corresponding to superclusters, galaxies, and star clusters, between today's observable universe and stellar systems. The self-similarity arises because, according to the virial theorem, the average gravitational potential energy per unit volume in each structural level is the same and depends only on the gravitational constant. The analysis indicates stellar systems first formed at z\approx62, consistent with the findings of Naoz et al, and self-similar large scale structures began to appear at redshift z\approx4. It outlines general features of development of self-similar large scale structures at redshift z<4. The analysis is consistent with observations for angular momentum of large scale structures as a function of mass, and average speed of substructures within large scale structures. The analysis also indicates relaxation times for star clusters are generally less than the age of the universe and relaxation times for more massive structures are greater than the age of the universe.Comment: Further clarification of assumptions underlying the analysi

New Embedded Representations and Evaluation Protocols for Inferring Transitive Relations

Beyond word embeddings, continuous representations of knowledge graph (KG) components, such as entities, types and relations, are widely used for entity mention disambiguation, relation inference and deep question answering. Great strides have been made in modeling general, asymmetric or antisymmetric KG relations using Gaussian, holographic, and complex embeddings. None of these directly enforce transitivity inherent in the is-instance-of and is-subtype-of relations. A recent proposal, called order embedding (OE), demands that the vector representing a subtype elementwise dominates the vector representing a supertype. However, the manner in which such constraints are asserted and evaluated have some limitations. In this short research note, we make three contributions specific to representing and inferring transitive relations. First, we propose and justify a significant improvement to the OE loss objective. Second, we propose a new representation of types as hyper-rectangular regions, that generalize and improve on OE. Third, we show that some current protocols to evaluate transitive relation inference can be misleading, and offer a sound alternative. Rather than use black-box deep learning modules off-the-shelf, we develop our training networks using elementary geometric considerations.Comment: Accepted at SIGIR 201

Stable Clustering Ansatz, Consistency Relations and Gravity Dual of Large-Scale Structure

Gravitational clustering in the nonlinear regime remains poorly understood. Gravity dual of gravitational clustering has recently been proposed as a means to study the nonlinear regime. The stable clustering ansatz remains a key ingredient to our understanding of gravitational clustering in the highly nonlinear regime. We study certain aspects of violation of the stable clustering ansatz in the gravity dual of Large Scale Structure (LSS). We extend the recent studies of gravitational clustering using AdS gravity dual to take into account possible departure from the stable clustering ansatz and to arbitrary dimensions. Next, we extend the recently introduced consistency relations to arbitrary dimensions. We use the consistency relations to test the commonly used models of gravitational clustering including the halo models and hierarchical ans\"atze. In particular we establish a tower of consistency relations for the hierarchical amplitudes: $Q, R_a, R_b, S_a,S_b,S_c$ etc. as a functions of the scaled peculiar velocity $h$. We also study the variants of popular halo models in this context. In contrast to recent claims, none of these models, in their simplest incarnation, seem to satisfy the consistency relations in the soft limit.Comment: 21 pages, 4 figure